Notes from the Video

  • Matt Dancho, founder of Business Science
  • Introduced to H2O-3 via the AutoML package
  • Sample code in R shared
  • Sample forecasting project / Walmart Sales
  • Tidymodels standardize machine learning packages
  • Modeltime loads H2O
  • Multiple time series
  • Create a forecast time horizon, assess 52 weeks forecast
  • Create preprocessing steps, helps the H2O algos to find good features
  • Some columns are normalized from the pre-processing
  • Extracted Time related features (i.e. week number, day of the week, etc)
  • Initializes H2O-3 / Stacked Ensemble model will be the best but hard to interpret
  • Modeltime workflow starts with a table
  • Modeltime is an organiational tool
  • Modeltime Calibrate will extract the residuals of the models
  • Visualize the forecast on the test set generates nice charts
  • Built a single H2O-3 model to predict on 7 different time series
  • This is very scalable, instead of looping through everything
  • Refit the model on the entire training data and then did a forward walk of 52 weeks
  • Modeltime ecosystem was created to help with higher frequent time series, at scale, that’s automated